6 Steps for Data Cleaning and Why it Matters

So you’re working with data to measure and optimize your fleet program. Have you also added data cleaning to your routine? Here is a quick overview to get you started.

No matter the type of data — telematics or otherwise — data quality is important. Old and inaccurate data can have an impact on results. Data cleaning, also called data cleansing, is the process of ensuring that your data is correct, consistent and usable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again.

The manual part of the process is what can make data cleaning an overwhelming task. While much of data cleaning can be done by software, it must be monitored and inconsistencies reviewed. This is why building a protocol for data cleaning is imperative.

Benefits of Data Cleaning

Here are several key benefits that come out of the data cleaning process:

It removes major errors and inconsistencies that are inevitable when multiple sources of data are getting pulled into one dataset.

Using tools to cleanup data will make everyone more efficient since they’ll be able to quickly get what they need from the data.

Fewer errors means happier customers and fewer frustrated employees.

The ability to map the different functions and what your data is intended to do and where it is coming from your data.

The first step when thinking of starting a data cleaning project is to first look at the big picture. Ask yourself: What are your goals and expectations?

6 Steps to Data Cleaning

To achieve your goals and meet expectations on how your fleet data can benefit you, you must first determine how will you execute data cleanup successfully. A couple of great guidelines to follow is to focus on your top metrics. What is your company’s overall goal and what is each member looking to achieve from it? A good way to start is to get all the interested parties involved and start throwing ideas around.

Here are some best practices when it comes to creating a data cleaning process:

1. Monitor ErrorsKeep a record and look at trends of where most errors are coming from, as this will make it a lot easier to identify fix the incorrect or corrupt data. This is especially important if you are integrating other solutions with your fleet management software, so that errors don’t clog up the work of other departments.

2. Standardize Your ProcessesIt’s important that you standardize the point of entry and check the importance of it. By standardizing your data process you will ensure a good point of entry and reduce the risk of duplication.

3. Validate AccuracyValidate the accuracy of your data once you have cleaned your existing database. Research and invest in data tools that allow you to clean your data in real-time. Some tools now even use AI or machine learning to better test for accuracy.

4. Scrub for Duplicate DataIdentify duplicates, since this will help you save time when analyzing data. This can be avoided by researching and investing in different data cleaning tools, as mentioned above, that can analyze raw data in bulk and automate the process for you.

5. AnalyzeAfter your data has been standardized, validated, and scrubbed for duplicates, use third-party sources to append it. Reliable third-party sources can capture information directly from first-party sites, then clean and compile the data to provide more complete information for business intelligence and analytics.

6. Communicate with the TeamCommunicate the new standardized cleaning process to your team. Now that you’ve scrubbed down your data, it’s important to keep it clean. This will help you develop and strengthen your customer segmentation and send more targeted information to customers and prospects, so you want to make sure you get your team in line with it.

Get Your ROI from Data

When you have the task of managing data, keeping on top of consistency and accuracy are two underlying jobs you have to deal with everyday. These steps should help make it easier to create a daily protocol. Once you have completed your data cleaning process, you can confidently move forward using the data for deep, operational insights now that your data is accurate and reliable.